_base_ = [
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

model = dict(
    type='ATSS',
    backbone=dict(
        type='ResNet',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained',
                      checkpoint='../anomaly_detection/resnet101-5d3b4d8f.pth')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='ATSSHead',
        num_classes=12,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    train_cfg=dict(
        assigner=dict(type='ATSSAssigner', topk=9),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=100))

dataset_type = 'CocoDataset'
classes = ('脚短', '偏位', '少件', '立碑', '侧立', '反向', '脚长', '错件', '溢胶', '铆钉不良', '翻件', '原材不良')

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=[(416, 416), (576, 576)], keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=[(416, 416)],
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]

train_dataset = dict(
        type=dataset_type,
        classes=classes,
        ann_file='/home/jiankai-cheng/teamdata/gj_datasets/hongjiao_20230817_coco/train/annotations/instances_annotations.json',
        img_prefix='/home/jiankai-cheng/teamdata/gj_datasets/hongjiao_20230817_coco/train/images/',
        pipeline=train_pipeline)

test_dataset = dict(
        type=dataset_type,
        classes=classes,
        ann_file='/home/jiankai-cheng/teamdata/gj_datasets/hongjiao_20230817_coco/test/annotations/instances_annotations.json',
        img_prefix='/home/jiankai-cheng/teamdata/gj_datasets/hongjiao_20230817_coco/test/images/',
        pipeline=test_pipeline)

train_concat_dataset = dict(
        type='ConcatDataset',
        datasets=[])

test_concat_dataset = dict(
        type='ConcatDataset',
        datasets=[])

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=train_concat_dataset,
    val=test_concat_dataset,
    test=test_concat_dataset)


optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=40)

evaluation = dict(interval=1, metric='bbox')
checkpoint_config = dict(interval=1)


custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')